Bottom Line:
We used biologically-constrained simulations to explore this issue, taking advantage of a peculiar pattern of CPs exhibited by multisensory neurons in area MSTd that represent self-motion.Although models that relied on correlated noise or selective decoding could both account for the peculiar pattern of CPs, predictions of the selective decoding model were substantially more consistent with various features of the neural and behavioral data.While correlated noise is essential to observe CPs, our findings suggest that selective decoding of neuronal signals also plays important roles.

ABSTRACTTrial by trial covariations between neural activity and perceptual decisions (quantified by choice Probability, CP) have been used to probe the contribution of sensory neurons to perceptual decisions. CPs are thought to be determined by both selective decoding of neural activity and by the structure of correlated noise among neurons, but the respective roles of these factors in creating CPs have been controversial. We used biologically-constrained simulations to explore this issue, taking advantage of a peculiar pattern of CPs exhibited by multisensory neurons in area MSTd that represent self-motion. Although models that relied on correlated noise or selective decoding could both account for the peculiar pattern of CPs, predictions of the selective decoding model were substantially more consistent with various features of the neural and behavioral data. While correlated noise is essential to observe CPs, our findings suggest that selective decoding of neuronal signals also plays important roles.

fig3s3: Noise correlation structure of the selective decoding model computed from the signal correlations of all distinct pairings of 129 neurons that were recorded previously by Gu et al. (2011).For the selective decoding model, correlated noise depends on rsignal in both stimulus conditions. As a result, the relationship between rnoise and rsignal is strong for pairs with matched congruency in both stimulus conditions (black), and this relationship is weak for pairs with mismatched congruency (gray).DOI:http://dx.doi.org/10.7554/eLife.02670.011

fig3s3: Noise correlation structure of the selective decoding model computed from the signal correlations of all distinct pairings of 129 neurons that were recorded previously by Gu et al. (2011).For the selective decoding model, correlated noise depends on rsignal in both stimulus conditions. As a result, the relationship between rnoise and rsignal is strong for pairs with matched congruency in both stimulus conditions (black), and this relationship is weak for pairs with mismatched congruency (gray).DOI:http://dx.doi.org/10.7554/eLife.02670.011

Bottom Line:
We used biologically-constrained simulations to explore this issue, taking advantage of a peculiar pattern of CPs exhibited by multisensory neurons in area MSTd that represent self-motion.Although models that relied on correlated noise or selective decoding could both account for the peculiar pattern of CPs, predictions of the selective decoding model were substantially more consistent with various features of the neural and behavioral data.While correlated noise is essential to observe CPs, our findings suggest that selective decoding of neuronal signals also plays important roles.

ABSTRACTTrial by trial covariations between neural activity and perceptual decisions (quantified by choice Probability, CP) have been used to probe the contribution of sensory neurons to perceptual decisions. CPs are thought to be determined by both selective decoding of neural activity and by the structure of correlated noise among neurons, but the respective roles of these factors in creating CPs have been controversial. We used biologically-constrained simulations to explore this issue, taking advantage of a peculiar pattern of CPs exhibited by multisensory neurons in area MSTd that represent self-motion. Although models that relied on correlated noise or selective decoding could both account for the peculiar pattern of CPs, predictions of the selective decoding model were substantially more consistent with various features of the neural and behavioral data. While correlated noise is essential to observe CPs, our findings suggest that selective decoding of neuronal signals also plays important roles.